Crack detection plays an important role in the industrial inspection of stamped metal products. While supervised learning methods are commonly used in the quality assessment process, they often require a substantial amount of labeled data, which can be challenging to obtain in a well-tuned production line. Unsupervised learning has demonstrated exceptional performance in anomaly detection. This study proposes an unsupervised algorithm for crack detection on stamped metal surfaces, capable of classification and segmentation without the need for crack images during training. The approach leverages the Vector Quantized-Variational Autoencoder 2 (VQ-VAE2) based model to reconstruct input images, while retaining crack details. Additionally, latent features at different scales are quantized into discrete representations using a codebook. To learn the distribution of these discrete representations from non-crack samples, the study utilizes PixelSNAIL, an autoregressive model used for sequential modeling. In the testing stage, the model assigns low probabilities to discrete features that deviate from the non-crack distribution. These potential crack candidate features are resampled using vectors in the codebook that exhibit the highest dissimilarity. The edited representations are then fed into the decoder to generate resampled images that have the most significant differences in the crack area from the original reconstruction. Crack patterns are extracted at the pixel level by subtracting resampled images from the reconstruction. Prior knowledge that crack patterns often appear darker is leveraged to enhance the crack features. A robust classification criterion is introduced based on the probability given by the autoregressive model. Extensive experiments were conducted using images captured from stamped metal panels. The results demonstrate that the proposed technique exhibits robust performance and high accuracy.

中文翻译：

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基于鲁棒无监督学习的冲压金属产品裂纹检测

裂纹检测在冲压金属制品的工业检测中发挥着重要作用。虽然监督学习方法通​​常用于质量评估过程，但它们通常需要大量标记数据，而在经过良好调整的生产线中获取这些数据可能具有挑战性。无监督学习在异常检测方面表现出了卓越的性能。本研究提出了一种用于冲压金属表面裂纹检测的无监督算法，能够在训练过程中无需裂纹图像即可进行分类和分割。该方法利用基于矢量量化变分自动编码器 2 (VQ-VAE2) 的模型来重建输入图像，同时保留裂纹细节。此外，使用码本将不同尺度的潜在特征量化为离散表示。为了从非裂纹样本中了解这些离散表示的分布，该研究利用了 PixelSNAIL，这是一种用于顺序建模的自回归模型。在测试阶段，模型将低概率分配给偏离非裂纹分布的离散特征。使用码本中表现出最高相异性的向量对这些潜在的裂纹候选特征进行重新采样。然后将编辑后的表示输入解码器以生成重新采样的图像，这些图像的裂纹区域与原始重建具有最显着的差异。通过从重建中减去重采样图像，在像素级别提取裂纹图案。利用裂纹图案通常显得较暗的先验知识来增强裂纹特征。基于自回归模型给出的概率引入了稳健的分类标准。使用从冲压金属面板捕获的图像进行了广泛的实验。结果表明，所提出的技术具有鲁棒的性能和高精度。